Scale dependency of insect assemblages in response to landscape ...

Landscape Ecology 19: 41–57, 2004. © 2004 Kluwer Academic Publishers. Printed in the Netherlands.

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Research article

Scale dependency of insect assemblages in response to landscape pattern Guillem Chust1,2,*, Joan Ll. Pretus1, Danielle Ducrot2 and Daniel Ventura3 1Department

of Ecology, Faculty of Biology (University of Barcelona), Diagonal 645, 08028 Barcelona, Spain; 2Centre of Space Studies of Biosphere (CNES-CNRS-IRD-UPS), 18, Avenue Edouard Belin; 31055 Toulouse Cedex, France; 3Department of Zoology, Faculty of Biology (University of Barcelona), Diagonal 645, 08028 Barcelona, Spain; *Author for correspondence (e-mail: [email protected]) Received 4 December 2001; accepted in revised form 8 July 2003

Key words: Diptera, Fragmentation, Homoptera, Landscape pattern, Remote sensing, Scale, Spatial indices, Species richness

Abstract Patches and their boundaries are sensitive to the scale at which they are viewed. The response of species to patchiness may depend on the resolution and on the extent by which the spatial pattern is perceived. The goal of this paper is to identify the scale at which forest spatial pattern causes changes in species richness and abundances of Dipteran and Homopteran species as a whole, and further on their distinctive ecological functional groups. Using remotely-sensed optical imagery, we described the landscape structure surrounding sampling sites. We used two approaches to deal with the problem of the scale of observation: 1兲 variation of extent using a multiscale analysis, and 2兲 comparison of two satellite sensors with different spatial resolutions 共SPOT: 20 ⫻ 20 m, and Landsat TM: 30 ⫻ 30 m兲. The relationship between entomological data and landscape descriptors at different spatial scales was tested with the Mantel test, redundancy analysis and stepwise multiple linear regression. Relative abundances of Homopteran species were affected by landscape patterns at finer scales than in Diptera. The strength of response to landscape was different for each Dipteran functional group. The multiscale analysis also enabled the optimal scale 共6.25 ha兲 of landscape pattern, accounting for 62% of the variation in Homopteran richness, to be identified. As a practical application, Homopteran richness was mapped by extrapolation of the regression function to the pixels of the image. Multiscale analysis provides an alternative view of fragmentation effects, which are traditionally studied through the patch-based approach, and highlights the importance of scale in ecological processes. The detection of optimal scales and the use of satellite images enable maps of important biotic indicators to be drawn up.

Introduction Landscape patches are delineated by boundaries that occur when structural or functional properties of ecological systems change discontinuously in space. The flow of organisms across these boundaries, along with other factors, affects patch colonisation and determines biotic diversity within patches 共Davies et al. 2001兲. Boundary flows are determined by: 共1兲 the probability that organisms encounter the boundary, and 共2兲 the probability that, once encountered, a

boundary will be crossed 共Wiens 1992兲. The second factor is modulated by several factors, including the permeability of the boundary, the perception of the boundary, and the selection of the patch type by the organism. Whether an organism will respond to a boundary by altering its movement, in a way that the researcher can discern, depends on whether it actually perceives the boundary either as a gentle gradient or as a sharp discontinuity. Thus, an organism’s perception may be an essential parameter if we are to understand the effects of the forest fragmentation on

42 species richness. Patches and boundaries, however, are sensitive to the scale at which they are viewed 共Wiens 1989兲. What is a complex mosaic of patches at one scale may disappear at either finer or broader scales of resolution. The spatial scale is determined by its ‘grain’, the lower limit of resolution, and its ‘extent’, the upper limit of resolution. These considerations emphasise the importance of scale in ecology, particularly in defining species habitat 共Kolasa and Waltho 1989; Wiens and Milne 1989; Cornell and Lawton 1992; Roland and Taylor 1997兲 and in building a general theory of diversity 共Whittaker et al. 2001兲. Wiens 共1992兲 proposed a link between the scale of landscape perception and the mobility of organisms. He suggested that the perception of the boundary by organisms becomes more likely as boundary contrast increases, while boundary perception for a given degree of contrast is greater for a less mobile than for a highly mobile organism. Because they encounter fewer boundaries, relatively sedentary organisms may perceive a higher contrast between patches than more mobile ones. In this sense, we expect that a highly mobile species should respond to landscape pattern at broader spatial scales than would a less-mobile group. Further, an ecological functional group, which involves a particular foraging behaviour and so mobility, might also respond in a specific manner to landscape pattern. Despite there are evidences that effects of habitat structure on insects differs according to species dispersal abilities 共e.g. Zschokke et al. 2000兲 and that landscape pattern affects insect movement 共e.g. Collinge and Palmer 2002兲, little attention has been paid in relating the scale of habitat structure with organism mobility in insects. The objective of this paper is to identify at which scales the spatial pattern of forest affects species richness and relative abundances of two insect groups: Diptera and Homoptera. Though several studies have already reported the effects of forest fragmentation on the abundance and species richness of various insect groups 共Rusek 1992; Didham et al. 1996; Ozanne et al. 1997; Zschokke et al. 2000; Davies et al. 2001; Magura et al. 2001; SteffanDwenter et al. 2002; Laurance et al. 2002兲, few of these dealt with the scale problem 共but see Roland and Taylor 1997; Elliott et al. 1998; Gering et al. 2003兲. Here, we are interested in comparing the scale and the strength of response of Homopteran and Dipteran assemblages to landscape. The influence of landscape pattern on the flow of organisms may af-

fect species richness differently than species composition; thus the response of these complementary descriptors of community structure to the landscape scale should be different. As mentioned above, we also suspect that functional groups might respond in a different manner to landscape pattern and spatial scales. We used two approaches to address the spatial scale problem: 1兲 variation of extent using a multiscale approach 共a methodologically refined version of that in Chust et al. 2000; see also Pearman 2002兲 and 2兲 variation of grain by using two satellite sensors with different spatial resolutions 共Landsat TM: 30 ⫻ 30 m, and SPOT: 20 ⫻ 20 m兲. The multiscale approach is based on an analysis of spatially nested landscapes surrounding each sampling site to calculate satellite-derived landscape descriptors. This approach is an alternative to the patch-based approach, which implicitly assumes that human-defined patches correspond to the organism’s perception of patch boundaries. We selected remotely sensed imagery as the source of land information to derive landscape indices. The multispectral mode of satellite optical data gives rich and detailed land information. Previous studies reported the possibilities of assessing biodiversity and species occurrence by satellite imagery 共Ormsby and Lunetta 1987; Pereira and Itami 1991; Stoms and Estes 1993; Jørgensen and Nøhr 1996; Cardillo et al. 1999; Gulinck et al. 2000; Saveraid et al. 2001; Turner et al. 2001; Bawa et al. 2002; Luoto et al. 2002兲. Most of these studies concern vertebrates and plants. Few of these studies are carried out on arthropod species 共but see Cumming 2000; Chust et al. 2003兲. From the perspective of applied research, the interest in using space technology lies in the possibility of providing spatial information such as maps of biodiversity indicators.

Material and methods Study area The Biosphere Reserve of the Island of Minorca in the Balearic archipelago 共Spain兲 is a Mediterranean agricultural landscape. Located in the Western Mediterranean 共Figure 1兲, Minorca is 701 km2 with a low relief surface. Médail and Quézel 共1997兲 and Myers et al. 共2000兲 included the island within a hotspot sector; that is, it contains an exceptional concentration

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Figure 1. The study area of Minorca, Spain. The image was acquired for the panchromatic mode of the SPOT satellite sensor 共spatial resolution: 10 ⫻ 10 m兲. The black matrix shows water surfaces. The brightest tones correspond to urban areas and bare ground, and tones from light grey to dark represent increasingly forested lands 共from agricultural areas and fallow land to shrubland and woodland兲.

of species and has a high rate of endemism. The vegetation of Minorca is evergreen due to its typically Mediterranean climate, with dry summers, mild winters and maximum rainfall in early autumn. The present ‘climax’ vegetation of Minorca comprises two main communities 共Bolós et al. 1970兲: a forest community dominated by holm oak 共Quercus ilex兲 and related shrubland, and a shrubland community characterised by wild olive 共Olea europaea var. sylvestris兲 which is adapted to drier conditions. The long-term human occupation of Minorca 共the first megalithic settlement flourished within the fourth millennium B.P.兲 has strongly influenced its biological and landscape characteristics. The sclerophyllous communities have been partially transformed to woodland of Pinus halepensis and to pastures. At present, pastures and agricultural land surrounded by

semi-natural vegetation cover more than half the island. These land-use changes have created a heterogeneous landscape mosaic 共Figure 1兲. Satellite data and land information We used optical images from two high-resolution satellite sensors that cover the island of Minorca to acquire ground data at different spatial resolutions: SPOT XS 共29th July 1994兲, SPOT XS and Panchromatic 共PAN兲 共29th October 1994兲, and Landsat TM 共14th July 1984兲. SPOT XS has a spatial resolution of 20 ⫻ 20 m and records the image on three spectral bands 共green, red and near infrared兲; PAN has 10 ⫻ 10 m spatial resolution, with one spectral band; Landsat TM has 30 ⫻ 30 m spatial resolution, with seven spectral bands. Given the limited spectral reso-

44 lution of the SPOT PAN sensor, we focused on the comparison of two spatial resolutions: 20 ⫻ 20 m 共SPOT兲 and 30 ⫻ 30 m 共TM兲. Two types of land information were derived from satellite images: a vegetation index and a land-cover classification. The Normalised Difference Vegetation Index 共NDVI兲 is defined as 共NIR-Red兲/共NIR⫹Red兲 where NIR is the Near-Infrared wavelengths 共band 3 in XS, band 4 in TM兲 and Red is the red wavelengths 共band 2 in XS, band 3 in TM兲. NDVI is an indicator of the presence and photosynthetic activity of green vegetation and is related to biomass and percentage ground cover 共Lillesand and Kiefer 2000兲. It is bounded between ⫺ 1 and 1; vegetated areas will generally yield positive values, while water results in negative values. Rock and bare soil give values near zero. The advantage of the normalised index is that it mitigates the effect of shadows in radiometric response. The NDVI and related indices have been used to predict bird assemblage composition 共Casucci et al. 1998兲, the occurrence of bird species 共Lauga and Joachim 1992兲, and species richness of tropical trees 共Bawa et al. 2002兲. Land-cover data were acquired through a supervised classification into two groups 共Chust 2002兲: forested land 共pine and holm oak wood, wild olive shrubland, dwarf shrubs兲 and non-forested land 共fallow land, agricultural areas, sandy and rocky habitats, urban areas, water surfaces兲. The landscape was dominated by fallow and agricultural lands 共51.1%兲, and forested land 共33.8%兲. Forested land was classified 共94.4% accuracy for SPOT and 97.8% for TM兲 with the maximum likelihood procedure, using spectral bands, spectral-derived textural information, morphometric indices and ancillary data 共slope of relief兲 共Chust 2002兲. Sampling procedures and diversity descriptives Adult insects 共Diptera and Homoptera兲 were sampled at 25 sites in mid-Spring 1998 共19-29 May; Chust 2002兲, by sweeping over plants in a square area of 10 ⫻ 10 m for 135 minutes 共i.e. 3 samples of 45 minutes at each site兲. The sites were located in forest patches that varied in size from small fragments of 100 m2 to large woods 共ⵑ2700 ha兲 and were embedded in landscapes that differed in degree of forest fragmentation. That is, we selected forested sites that were surrounded by forest, by a mosaic of forest patches and agricultural land, and by agricultural land alone. Forest comprised the main four types of Medi-

terranean sclerophyllous vegetation of Minorca: holm oak wood, mixed forest of pine and holm oak, wild olive shrubland, and dwarf shrubs. Homoptera constitutes an ecologically homogeneous group; they are exclusively phytophagous. On the other hand, Diptera include different trophic guilds and their feeding habits may depend on lifecycle stage. For this reason, we distinguished three functional groups within Diptera based on larval feeding styles: phytophagous, predators and parasites, and saprophagous 共according to Papp and Darvas 1997; Papp and Darvas 1998; Papp and Darvas 2000, and Ferrar 1987兲. The reason for classifying Diptera according to the feeding habits of the larval stage is that some species do not feed in the adult stage. Larval feeding habits are also closely linked to the adult behaviour, since they determine how adult insects search the suitable sites for mating and egg deposition. Another relevant difference between Dipteran and Hompoteran species concerns mobility. Although empirical measures on dispersal are not well documented for most species sampled, it is of common experience that most of Dipteran species possess a greater capacity to move, relative to most of Homopteran species. For the entire study, a total of 116 Dipteran species 共37 phytophagous, 24 predators and parasites, 47 saprophagous, and 8 unknown兲 and 44 Homopteran species were identified at morphospecies level 共Table 1兲. We estimated species richness 共S兲 of each site by averaging number of species among the three samples. We did not considered the estimator ‘total species richness’ because of its high correlation, and so redundancy, with the average of three samples 共r ⫽ 0.97 for Diptera, r ⬎ 0.96 for each of the three Dipteran functional groups, r ⫽ 0.81 for Homoptera兲. No differences in species richness were found between the four forest types 共p ⬎ 0.1 for each of the five insect groups兲, indicating species were sampled in homogenous habitat type. Insect groups differed slightly in mean body size 共Diptera: 3.8 mm, phytophagous: 3.0 mm, predators and parasites: 6.1 mm, saprophagous: 3.5 mm, Homoptera: 3.7 mm兲. Multiscale approach: extraction of landscape descriptors A multiscale approach was developed to test the relationship between species abundances and richness and landscape pattern. We used spatially nested landscapes surrounding each sampling site to calculate

45 Table 1. Number of morphospecies within families of Diptera and Homoptera. Diptera species were categorised by larval functional groups. Cicadellidae has the greatest number of species within the Homoptera family, but species are more evenly distributed among Dipteran families. Phy: phytophagous, Pre: predators, Par: parasites, Sap: saprophagous, Unk: unknown. Diptera Family Agromyzidae Asilidae Asteiidae Carnidae Chloropidae Dolichopodidae Drosophilidae Heleomyzidae Lauxaniidae Lonchaeidae Lonchopteridae Muscidae Opomyzidae Pipunculidae Scathophagidae Sciaridae Sciomyzidae Sepsidae Sphaeroceridae Syrphidae Tachinidae Tephritidae Therevidae Trixoscelididae TOTAL

Homoptera Number of species and larval functional group 17 Phy 2 Pre 1 Sap 1 Sap 3 Phy, 3 Sap, 1 Dep, 8 Unk 3 Pre 3 Sap 2 Sap 3 Sap 1 Sap 1 Sap 7 Pre, 4 Sap 1 Phy 2 Par 2 Pre 11 Sap 1 Par 3 Sap 7 Sap 3 Sap, 1 Phy 5 Par 15 Phy 1 Pre 4 Sap 116

landscape descriptors. This procedure, used by Bergin et al. 共2000兲 and Pearman 共2002兲, is based on the fact that landscape description at a specified scale contains information about landscapes at finer scales. Spatial indices 共described below兲 were then computed from the thematic maps for a square window of n ⫻ n pixels 共where n ⫽ 3, 5, 7, ...兲 centred on the sample site. The maximum area of 1056 ha 共3250 ⫻ 3250 m window size兲 was imposed by the spatial extent of the images. Thus, the landscape extent of our analysis varied across a wide range of spatial scales 共from 0.36 to 1056 ha兲. We calculated 4 spatial indices from the NDVI: Mean 共M兲, giving the central tendency of the vegetation index. Standard Deviation 共SD兲, as a measurement of NDVI heterogeneity. Angular Second Moment 共ASM兲, as a measurement of the spatial homogeneity of the vegetation index: ASM ⫽

兺 兺 关p共i, j兲兴2

i⫽1,n j⫽1,n

Family Aleyrodidae Cercopidae Cicadellidae Cixiidae Delphacidae Issidae Psyllidae Tettigometridae Triozidae

Number of species 2 2 21 2 3 4 8 1 1

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where p(i,j) is the co-occurrence probability between classes i and j 共Haralick et al. 1973兲, i.e. the probability that pixel of class i is adjacent to pixel of class j, and n is the number of classes. ASM is bounded between 0 and 1. Adjacency includes the four nearest neighbours in any direction. While SD does not take into account the contagion among adjacent pixels, ASM measures the different arrangement of pixels within the window and so, unlike SD, incorporates a spatial component. SD and ASM are related to edge measurement 共total length or number兲. For example, intermediate levels of forest fragmentation are equivalent to extreme values of edge indices, SD and ASM. Mean Distance 共MD兲, a measurement of the difference between an estimate of local habitat and the surrounding environment. MD is calculated as follows: MD ⫽

1 N

兺

i⫽1,N

¯ⱍ ⱍNDVIi ⫺ Y

where NDVIi is the NDVI value of the pixel i within the window, and N is the total number of pixels in

46 the window. The estimate of the local habitat ¯ Y is previously obtained by averaging NDVI values for homogeneous areas of forested lands 共i.e. where Diptera and Homoptera were sampled兲. Thus, MD avoids the problem of sampling in small patches when they are not detected by the satellite sensor. The index varies from 0 to the variable domain; i.e. 2 for ¯ is a constant, MD behaves as a low-pass NDVI. As Y filter operator 共like M兲 and not as a high pass filter like SD. We calculated a fifth statistic from the land-cover classification: Forest Proportion 共FP兲, the number of pixels belonging to forested lands 共i.e. the kinds of forest where Diptera and Homoptera were sampled兲 divided by the total number of pixels in the window. Increasing values of FP mean that the surrounding environment is more forested. These spatial indices can be classified into three types for the purpose of landscape description: vegetation cover 共M兲, landscape heterogeneity 共SD, ASM兲 and habitat dominance 共MD, FP兲. Multiscale approach: relationship between species data and landscape descriptors Our concern was the relationship between entomological data and landscape descriptors at different spatial extents and at different spatial resolutions. Species abundances was studied by means of the Mantel test and Redundancy analysis 共explained below兲; species richness, by stepwise multiple linear regression analysis. We compared the response to landscape of Homoptera, Diptera and the three functional groups of Diptera previously described. Analysis of species abundances We used the Mantel test, as implemented in the statistical program R-Package 4.0 共Casgrain and Legendre 2000兲, to determine whether the correlation between the similarity in species abundances and in landscape descriptors differs across scales. The Mantel test is a method for comparing two similarity or distance matrices, computed for the same sites 共Legendre and Legendre 1998兲. The Mantel statistic 共rM兲 is a measurement of the correlation between the two similarity matrices and results from the cross-product of the matrix elements after standardisation. rM varies from ⫺ 1 to ⫹1 and behaves like a correlation coefficient. The Mantel statistic is tested by permuta-

tion, thus avoiding distributional assumptions of data as required in parametric tests 共e.g. normality兲. For our data, each element of the species matrix 共m species by n sites兲 represents the abundance of a species at a site, obtained by pooling the three samples of each site. In the environmental matrix 共5 variables: 兵X1,X2,...,X5其 by n sites兲, each element represents the value of one of the 5 landscape descriptors 共M, SD, ASM, MD, FP兲. The similarity matrix of species 共n x n兲 was obtained from the similarity coefficient of Steinhaus, an asymmetrical quantitative coefficient defined as the sum of the minimum abundance of the various species, divided by the total number of observed specimens. A similarity matrix of environmental data was obtained from the Gower coefficient to quantify the similarity of habitats surrounding the sampling sites. Gower’s coefficient is appropriate for quantitative data, and is a symmetrical coefficient 共i.e. the state zero for two objects is treated in exactly the same way as any other pair of values兲. The simplest form of Gower coefficient 共SG兲 is as follows: SG共site1,site2兲 ⫽

1

p

兺 S12j pj⫽1

where p is the number of variables and S12j = 1 – 共|X1j – X2j|/Rj兲, which is a normalised distance transformed into a similarity with Rj the largest difference of the variable. Legendre and Legendre 共1998兲 provide a detailed description of these similarity coefficients. Redundancy analysis 共RDA兲 is a canonical ordination technique that extracts continuous axes of variation from species abundance data in order to explain which portion of this variation is directly explained by environmental variables 共Ter Braak and Smilauer 1998兲. Such ordination axes are constructed under the restriction of being linear combinations of environmental variables. In RDA, species are assumed to have linear response surfaces with respect to compound environmental gradients. Thus, RDA is a direct extension of multiple regression to the modelling of multivariate response data. The forward selection option was used to select those environmental variables that significantly explain a portion of species variance. Statistical significance was tested by Montecarlo permutations. The analysis was run with the canonical community ordination software CANOCO 4.0 共Ter Braak and Smilauer 1998兲. The reason to use both the Mantel test and RDA was twofold: first, to compare results of independent

47 methodologies in our data and, second, because the Mantel test gives an overall measurement of landscape pattern 共since it includes all spatial indices兲, while RDA selects only those spatial indices that significantly explain the variance of species abundances. Analysis of species richness and mapping Finally, we tested the relationship between species richness 共S兲 and landscape descriptors 共Xi兲 by stepwise multiple linear regression 共Zar 1996; Legendre and Legendre 1998兲 for each spatial scale. First, we selected those variables that were individually correlated with S. Then, we applied the stepwise procedure that tests different models by including and eliminating explanatory variables in steps, and selects the model where the multiple correlation coefficient 共R兲 is the highest and where only the significant partial regression coefficients are retained 共i.e. ␤ ⫽ 0兲. Therefore, we obtained a model for each scale s 共s ⫽ 1,2,...,n兲: Sˆs ⫽ B0 ⫹

m

兺 BiXi,s i⫽1

where B0 and Bi are the estimates of ␤0 and ␤i, respectively, m is the number of landscape descriptors included in the stepwise regression, and n is the number of scales included in the study 共i.e. 53 for TM and 80 for SPOT兲. In order to determine the optimal scales 共those explaining most of the variance in species richness兲, we performed stepwise multiple linear regression between S and 兵Sˆ 1, Sˆ 2,..., Sˆ n其. The resulting model is as follows: Sˆ ⫽ C0 ⫹

n

兺 CsSˆs s⫽1

where Cs are the new regression coefficients that provide the relative weight of each spatial scale accounting for S. This is considered the optimal model 共maximal R2 with significant regression coefficients兲 for predicting species richness. Because the estimation of S depends on Sˆ s and not directly on Xi, the model can be considered as nested. The reason for proceeding in this way is that an estimate of S which included all variables and all scales at the same time could result in spurious correlation, given the large number of combinations 共5 spatial indices by 80 scales for SPOT兲.

Because regression analysis requires a Gaussian 共normal兲 distribution of the dependent variables, we performed the Kolmogorv-Smirnov test to ensure that this assumption was met. A normal distribution was found for all species groups 共p ⫽ 0.128 for overall Diptera, p ⫽ 0.305 for phytophagous, p ⫽ 0.474 for predators and parasites, p ⫽ 0.827 for saprophagous, and p ⫽ 0.343 for Homoptera兲.

Results Analysis of species abundances The Mantel test revealed that similarities in the relative abundances of Diptera 共all species兲 correlated poorly with similarities in landscape descriptors 共Figure 2兲 in both SPOT and TM spatial resolutions across a range of spatial scales 共maximal rM value is 0.16, p ⫽ 0.017, at 3030 ⫻ 3030 m window size; Figure 3兲. Two scale ranges of response can be distinguished in SPOT, while in TM the local scale was not detected. For Dipteran functional groups, predators and parasites and saprophagous Dipterans were not correlated with landscape descriptors at any scale in both SPOT and TM. The phytophagous group was significantly correlated with landscape descriptors at few scales and only for TM; these significant scales were not contiguous and P-values were around 0.04, indicating that the correlation was not robust. In contrast, Homoptera showed higher rM maximal values 共Figure 2兲: 0.30 共for SPOT, p ⫽ 0.001兲 and 0.36 共for TM, p ⫽ 0.001兲 at local scales 共ⵑ150 ⫻ 150 m兲 共Figure 3兲. The Mantel statistic decreased with window size from these local optimal scales until it was no longer significant beyond ⵑ750 ⫻ 750 m. In Redundancy analysis 共RDA兲, the explained variance in Dipteran assemblages was low 共Figure 4兲, as in the case of the Mantel test. There also existed two significant scale ranges in SPOT, at finest and broadest scales, and only at broadest scales in TM. The optimal scale was found at 100 ⫻ 100 m in SPOT resolution where the SD landscape descriptor alone explained 13.5% of the variance 共Table 2兲. Among the functional groups, predators and parasites showed a well-defined response with a clear peak at intermediate scales 共ⵑ1600 ⫻ 1600 m兲 which explained 28% of the variance of species abundances 共p ⬍ 0.020兲. The most explicative landscape descriptor was MD for SPOT resolution, and M for TM 共Table 2兲. In fact,

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Figure 2. Mantel correlation values 共rM兲, at different spatial scales, between relative abundances similarity and landscape descriptors similarity comparing Diptera 共all species and functional groups兲 and Homoptera. Significant rM values 共with p ⫽ 0.05 as the criterion兲 are indicated by solid symbols 共in black兲, and non-significant rM values by unfilled symbols. Landscapes descriptors are derived from SPOT images 共spatial resolution: 20 ⫻ 20 m兲 and from TM images 共spatial resolution: 30 ⫻ 30 m兲.

these two landscape descriptors are highly correlated at this scale range 共r ⫽ 0.98兲, indicating that they each explained species abundances of predators and parasites group similarly. The phytophagous group was less correlated with landscape descriptors and without a defined peak; M explained between 17% 共SPOT兲 and 20% 共TM兲 of the variance at intermediate scales: 1780 ⫻ 1780 m and 2010 ⫻ 2010 m, respectively. The saprophagous species were poorly explained by landscape descriptors 共13% of the variance at broad scales兲. Conversely, RDA revealed that Homopteran assemblages presented a clear response at the finest scales, which decreased gradually with window size. Species abundances were best explained by FP and

ASM at 90 ⫻ 90 m for TM 共28.1%, p ⫽ 0.001兲, and by M at 60 ⫻ 60 m for SPOT 共16.5%, p ⫽ 0.001兲 共Table 2兲. At other fine scales, M and FP also significantly explained Homopteran assemblages, indicating that vegetation cover is the main factor affecting this insect group. In TM resolution, RDA detected another significant scale range 共around 2000 ⫻ 2000 m兲 where ASM explained 12% of the variance. In summary, the results of the Mantel test and RDA indicate that Dipterans 共all species兲 were little affected by landscape pattern, with a significant response evident at local 共only for SPOT兲 and at broad scales. Homopterans exhibited an association with the vegetation cover at fine scales. However, RDA revealed that when Dipteran assemblages were

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Figure 3. Steinhaus similarities in the relative abundances of species 共for Dipteran and Homopteran兲 between pairs of sites as a function of Gower similarities in landscape descriptors 共extracted from SPOT and TM satellite images兲. Landscape descriptors have been calculated at spatial scales that presented the highest Mantel correlation with species abundances.

analysed by functional group, all of them responded to landscape pattern at broad scales. In particular, predators and parasites showed a well-defined response. The Mantel test did not detect the response of functional groups, which indicates that functional strategies are sensitive to only one or a few landscape descriptors and not to a generic multidimensional measure of landscape pattern. Analysis of species richness and spatial modelling Regression analysis revealed that Dipteran species richness correlated poorly with landscape indices; only the first three scales are significant but have low

correlation values 共Figure 5兲. Correlation coefficients between species richness and landscape indices across spatial scales were similar for both TM and SPOT images. The optimal scales 共that is, those at which landscape descriptors explain most of the variance in species richness兲 were found at the finest spatial extents: 60 ⫻ 60 m for SPOT and 90 ⫻ 90 m for TM 共Table 3兲. The correlation was negative with M 共r ⫽ ⫺ 0.55 for SPOT, r ⫽ ⫺ 0.52 for TM; Figure 6兲, indicating that the dominance of vegetation adversely affected the number of Dipteran species. Species richness of Dipteran functional groups behaved differently in response to the landscape pattern descriptors. The richness of phytophagous Dipterans

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Figure 4. Redundancy analysis between relative abundances and landscape indices, at different spatial scales, to compare the response of Diptera and Homoptera assemblages to landscape pattern at different spatial scales. Solid symbols represent significant explained variance 共with p ⫽ 0.05 as the criterion兲, and unfilled symbols represent non-significant explained variance. Landscape descriptors are derived from SPOT images 共spatial resolution: 20 ⫻ 20 m兲 and from TM images 共spatial resolution: 30 ⫻ 30 m兲.

responded to landscape at the finest scale for SPOT resolution 共r ⫽ ⫹0.66, p ⬍ 0.001, with SD兲 共Table 3兲. However, we confirmed that this relatively high correlation was partially forced by outliers 共Figure 6兲. At this spatial resolution, a second significant peak was at the scale 300 ⫻ 300 m; this peak corresponded to the optimal scale for the TM spatial resolution 共r ⫽ ⫹0.53, p ⫽ 0.006, with MD; Figure 6兲. The richness of predators and parasites showed similar responses for both SPOT and TM. Despite a response at two scale ranges, however, the coefficient of correlation was very low and the optimal scale was at 2100 ⫻ 2100 m for SPOT 共r ⫽ ⫺ 0.43, p ⫽ 0.031, with SD兲 and at 150 ⫻ 150 m for TM 共r ⫽ ⫺ 0.47, p ⫽ 0.018 with FP兲. Species richness within the

saprophagous group showed a positive correlation with FP at broad scales 共r ⫽ ⫹0.47 for SPOT, r ⫽ ⫹0.52 for TM; Table 3兲. For Homopteran species richness, correlation with landscape indices was high at well-defined scales 共Figure 5兲. The response curves were similar for TM and SPOT. The optimal scale was at 250 ⫻ 250 m 共i.e. 6.25 ha兲 for SPOT 共r ⫽ ⫺ 0.79 with FP, p ⬍ 0.001兲 and 210 ⫻ 210 m for TM 共r ⫽ ⫺ 0.78 with FP兲 共Table 3, Figure 6兲. Homopteran species richness diminished with the amount of surrounding forest. Thus, their optimal environment seems to be characterised by scattered forest patches within a matrix of agricultural and fallow land.

51 Table 2. Redundancy analysis applied to Dipteran and Homopteran species 共abundance data兲 to explain which portion of this variation is directly explained by landscape indices. Spatial scales, at which landscape descriptors most explain the variance in species relative abundances, are given according to the spatial resolution of satellite sensor 共SPOT spatial resolution: 20 ⫻ 20 m, TM spatial resolution: 30 ⫻ 30 m兲. Landscape descriptors, M: mean, SD: standard deviation, ASM: angular second moment, MD: mean distance, FP: forest proportion.

Diptera

All species Phytophagous Predators and parasites Saprophagous

Homoptera

Satellite image

Landscape descriptors

Scale 共in m兲

Explained variance 共%兲

P

SPOT TM SPOT TM SPOT TM SPOT TM SPOT TM

SD FP M M MD M M M M FP, ASM

100 3330 1780 2010 1580 1650 3220 3330 60 90

13.5 10.7 17.2 20.2 28.1 28.2 13.4 13.4 16.5 28.1

0.019 0.010 0.011 0.004 0.017 0.018 0.028 0.027 0.001 0.001

As a practical application, we used the best model among the species groups studied, based on the prediction of Homopteran species richness 共Sˆ Homoptera = ⫺ 0.04FP(250 ⫻ 250m) ⫹ 9.75兲, as an example to show a predicted map of species richness. The proportion of forest, defined at the optimal scale, was used as an inverse indicator of Homopteran species richness, which, by extrapolating all pixels of the satellite image, allows the mapping of such an indicator 共Figure 7兲. The reliability of the spatial model depends on both the explained variance of the regression model and the accuracy of land-cover classification. Thus, the accuracy of the model in predicting Homopteran species richness was 58.9% using SPOT and 59.5% using TM. This type of model, and in particular the multiscale approach used here for mapping, can help environmental managers detect valuable biological indicators in poorly surveyed regions. Nevertheless, given that the spatial model of Homopteran species richness is an indicator of those species living in small and scattered forest patches, a conservation strategy based on such a model would seriously affect those species dependent upon interior forest. Moreover, additional terrain work would be necessary to test the predictions of the model more accurately, specially to be used by environmental managers. Given the results obtained in regression analysis, we considered that the prediction of Dipteran species richness 共for all species and for functional groups兲 is not accurately enough to make an extrapolation to image pixels.

Discussion and conclusions Analysis of relative abundances of species showed that the response of Homopteran assemblages to landscape pattern was centred at local spatial scales 共optimal scale: 0.36-2.25 ha兲, while the response of Dipteran groups were sensitive to landscape metrics at broader scales 共phytophagous: 316-404 ha, predators and parasites: 250-272 ha, saprophagous: 1056 ha兲. The greater capacity of most of Dipteran species to move, relative to most of Homopteran species, is in accordance with their corresponding scales of response to landscape. Moreover, the strength of response to landscape was different for each Dipteran functional group; in particular Dipteran predators and parasites showed a steeper response to landscape than the other two functional groups. This finding agreed with the statement that species of higher trophic levels, such as predators and parasites, are expected to be more prone to extinction with increased habitat fragmentation 共Steffan-Dwenter et al. 2002兲. The individual behaviour associated with each trophic level may also explain these differences. For instance, searching for prey or host across the landscape may require predators and parasites to possess an accurate discrimination of patches that could explain their strong response to habitat structure 共i.e. 28% of explained variance兲 at broader scales. The particular response of each functional group to landscape 共in terms of scale dependency and strength of response兲 has been also reported on several avian foraging guilds in tropical forest understory 共Pearman 2002兲. Analysis of species richness showed that Dipterans, considering all species and by functional groups, cor-

52

Figure 5. Multiple correlation coefficient 共R兲 based on a stepwise multiple linear regression analysis between species richness and landscape descriptors at different spatial scales. Solid symbols represent optimal scales 共that is, those spatial scales at which landscape descriptors explain most of the variance in species richness兲. Horizontal dashed line represents the value of R at a 5% significance level 共s.l.兲. Landscape descriptors are derived from SPOT images 共spatial resolution: 20 ⫻ 20 m兲 and from TM image 共spatial resolution: 30 ⫻ 30 m兲.

related poorly with landscape indices. In contrast, Homopteran species richness correlated strongly with landscape descriptors, accounting for 62% of the species richness variance. In particular, the environment containing the largest number of Homopteran species was characterised by a small forest patch surrounded by 6.25 ha of non-forested landscape. Studies dealing with the response of insect species richness to habitat area have showed its dependency on ecological and life history traits of each species group 共Laurance et al. 2002兲. Thus, species richness and abundance of generalist insects per unit area rose after fragment isolation 共Laurance et al. 2002兲 and with increasing landscape diversity 共Jonsen and Fahrig 1997兲, whereas other insect species 共certain beetles, ant,

bees, and butterflies兲 responded negatively to habitat loss and to edges effects 共Laurance et al. 2002兲. A strength of our study is that we could identify a landscape type with a defined spatial extent to predict local species richness. Beyond the ecological interpretation for Hompteran species in Mediterranean landscapes, the spatial extent of landscape at which biota is sensitive could be the basis to define buffer areas for nature reserves, as it has been proposed for endemic species richness of Pyrenean Collembola 共Chust 2002兲. Thus, the multiscale approach enables the identification of the spatial scales that most affect species richness and abundances of assemblages. Scales at which landscape influences assemblages are different

53 Table 3. Regression analysis applied to Dipteran and Homopteran species richness to explain which portion of this variation is explained by landscape descriptors. Optimal scales 共those explaining most of the variance in species richness兲 were determined using stepwise multiple linear regression between species richness 共S兲 and 兵Sˆ 1, Sˆ 2,..., Sˆ n其, where Sˆ s is the estimation of S at the spatial extent s. r is the coefficient of correlation. Abbreviations of landscape descriptors are explained in Table 2.

Diptera

All species Phytophagous Predators and parasites Saprophagous

Homoptera

Satellite image

Landscape descriptors

Optimal scale 共in m兲

r

SPOT TM SPOT TM SPOT TM SPOT TM SPOT TM

M M SD MD SD FP FP FP FP FP

60 90 60 270 2100 150 3140 2970 250 210

⫺ 0.55 0.005 ⫺ 0.52 0.007 ⫹0.66 ⬍ 0.001 0.006 ⫹0.53 ⫺ 0.43 0.031 ⫺ 0.47 0.018 0.019 ⫹0.47 0.008 ⫹0.52 ⫺ 0.79 ⬍ 0.001 ⫺ 0.78 ⬍ 0.001

for Homopterans than for Dipterans, among functional groups of Dipterans, and for species abundances as opposed to species richness. Differences in the spatial scale at which landscape affects species abundances versus species richness have been also found in Collembola 共Chust et al. 2003兲. The response of a taxonomic group to a wide range of scales, from local to broad scales, is also present in other insect groups. For example, Roland and Taylor 共1997兲 found that species of insect parasitoids would likely be most effective at scales ranging from 0.3 to 72 ha in forested landscapes. Previous studies have also showed a large scale effects of habitat structure on insects across a broad range of landscapes 共for herbivores Colleopterans and Homopterans: Jonsen and Fahrig 1997; for aphid predators: Elliot et al. 1998兲. It is important to remark, however, that the spatial extent at which species presented a response to landscape should not be confused with the absolute size of two-dimensional habitat patches 共Kolasa and Waltho 1998兲, thus making difficult to compare our results with experimental studies on habitat area and isolation 共such as Zschokke et al. 2000; or Laurance et al. 2002兲. Our findings highlight the importance of considering the spatial scale in the study of landscape effects on species assemblages. A multiscale approach provides an alternative and complementary view of fragmentation effects that are traditionally studied through patch-based approaches. A rendering of the effects of spatial grain on the detection of the response of assemblages to landscape resulted from a comparison of data obtained from two satellite sensors with different spatial resolutions. The analysis of both species abundances and richness showed similar patterns using SPOT and TM images.

P

This indicates that, overall, differences in the spatial resolution between these sensors are not so great as to detect large differences in landscape pattern affecting biotic parameters. However, there exist two situations where the response curves may differ qualitatively: at local scales, and when correlation between species data and landscape descriptors is low. In the first case, the coarser spatial resolution of TM was not able to capture the assemblage response as did SPOT, pointing out the importance of the grain size used. In the second case, low coefficients of correlation are very sensitive to small differences in landscape description extracted from the two sensors. The use of higher-resolution spatial sensors 共e.g. IKONOS兲 or of airborne sensors in future research should help broaden the analysis of the response of insect assemblages to the habitat structure at local scales. The multispectral capabilities of remotely sensed imagery were found to be adequate for landscape description and explained a significant fraction of species richness and the structure of assemblages; particularly for species richness and abundances of Homopterans and for the abundances of predator and parasite Dipterans. The space technology enabled to provide diversity maps, like that of Homopteran species of Minorca, which are the final products of interest for the natural area manager. They can be used, for instance, to identify ‘gaps’ in the network of conservation land. Beyond circumstantial and imperfect representation of local patterns of Homopteran biodiversity in Minorca, which might need additional terrain work to be used in conservation practices, it illustrates the power of a method. The whole approach developed in this paper can be conceived as a useful method in the framework of gap analysis

54

Figure 6. Relationships between species richness 共for all species of Dipterans, for phytophagous Dipterans and Homopteran species兲 and landscape descriptors 共extracted from SPOT and TM satellite images兲. Landscape descriptors have been calculated at spatial scales that presented the highest correlation with species abundances. Landscape descriptors: mean 共M兲, mean distance 共MD兲, standard deviation 共SD兲, forest proportion 共FP兲. The range of M, MD, and SD has been normalised. Solid lines represent linear regression models. The coefficients of correlation are given in Table 3.

55

Figure 7. Predicted species richness of Homoptera in Minorca, based on a multiscale analysis. Here, forest proportion, defined at the spatial scale of 250 ⫻ 250 m, is the best landscape descriptor for predicting Homopteran species richness 共Sˆ Homoptera = ⫺ 0.04FP(250 ⫻ 250m) + 9.75兲. Dark tones correspond to low values of forest cover 共close to 0%兲, which are predicted to have a high species richness of Homoptera. Brighter tones correspond to homogeneous forest 共forest cover close to 100%兲, where low Homopteran species richness is predicted. The grid reference system is UTM.

共Jennings 2000; Stoms 2000兲 since this approach would greatly reduce the costs of local biodiversity evaluation in terms of time and effort, in a domain where human resources are notoriously limited.

Acknowledgments Guillem Chust was supported by a scholarship from the University of Barcelona. We acknowledge anonymous reviewers for their helpful comments.

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